This prospective longitudinal study found that approximately one-third of participants showed chest CT findings with pulmonary fibrosis-like changes within 6 months of recovery from severe coronavi... Background Little is known about the long-term lung radiographic changes in patients who have recovered from coronavirus disease 2019 (COVID-19), especially those with severe disease. Purpose To pr...

Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To develop a highly sensitive deep learning-based algorithm that assists in the detection of cerebral aneurysms on CT angiography images. Materials and Methods Head CT angiography images were retrospectively retrieved from two hospital databases acquired across four different scanners between January 2015 and June 2019. The data were divided into training and validation sets; 400 additional independent CT angiograms acquired between July and December 2019 were used for external validation. A deep learning-based algorithm was constructed and assessed. Both internal and external validation were performed. Jackknife alternative free-response receiver operating characteristic analysis was performed. Results A total of 1068 patients (mean age, 57 years ± 11 [standard deviation]; 660 women) were evaluated for a total of 1068 CT angiograms encompassing 1337 cerebral aneurysms. Of these, 534 CT angiograms (688 aneurysms) were assigned to the training set, and the remaining 534 CT angiograms (649 aneurysms) constituted the validation set. The sensitivity of the proposed algorithm for detecting cerebral aneurysms was 97.5% (633 of 649; 95% CI: 96.0, 98.6). Moreover, eight new aneurysms that had been overlooked in the initial reports were detected (1.2%, eight of 649). With the aid of the algorithm, the overall performance of radiologists in terms of area under the weighted alternative free-response receiver operating characteristic curve was higher by 0.01 (95% CI: 0.00, 0.03). Conclusion The proposed deep learning algorithm assisted radiologists in detecting cerebral aneurysms on CT angiography images, resulting in a higher detection rate. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kallmes and Erickson in this issue.

Fibrotic interstitial lung abnormalities in survivors of severe COVID-19 depicted on 6-month CT scans were persistent on 1-year CT scans and were negatively correlated with the lung diffusion capacity.

Background Information on pulmonary sequelae and pulmonary function 2 years after recovery from SARS-CoV-2 infection is lacking. Purpose To longitudinally assess changes in chest CT abnormalities and pulmonary function in individuals after SARS-CoV-2 infection. Materials and Methods In this prospective study, participants discharged from the hospital after SARS-CoV-2 infection from January 20 to March 10, 2020, were considered for enrollment. Participants without chest CT scans at admission or with complete resolution of lung abnormalities at discharge were excluded. Serial chest CT scans and pulmonary function test results were obtained 6 months (June 20 to August 31, 2020), 12 months (December 20, 2020, to February 3, 2021), and 2 years (November 16, 2021, to January 10, 2022) after symptom onset. The term interstitial lung abnormality (ILA) and two subcategories, fibrotic ILAs and nonfibrotic ILAs, were used to describe residual CT abnormalities on follow-up CT scans. Differences between groups were compared with the χ² test, Fisher exact test, or independent samples t test. Results Overall, 144 participants (median age, 60 years [range, 27-80 years]; 79 men) were included. On 2-year follow-up CT scans, 39% of participants (56 of 144) had ILAs, including 23% (33 of 144) with fibrotic ILAs and 16% (23 of 144) with nonfibrotic ILAs. The remaining 88 of 144 participants (61%) showed complete radiologic resolution. Over 2 years, the incidence of ILAs gradually decreased (54%, 42%, and 39% of participants at 6 months, 12 months, and 2 years, respectively; P < .001). Respiratory symptoms (34% vs 15%, P = .007) and abnormal diffusing capacity of lung for carbon monoxide (43% vs 20%, P = .004) occurred more frequently in participants with ILAs than in those with complete radiologic resolution. Conclusion More than one-third of participants had persistent interstitial lung abnormalities 2 years after COVID-19 infection, which were associated with respiratory symptoms and decreased diffusion pulmonary function. Chinese Clinical Trial Registry no. ChiCTR2000038609 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by van Beek in this issue.

To retrospectively analyze the evolution of clinical features and thin-section CT imaging of novel coronavirus pneumonia (COVID-19) in 17 discharged patients. Serial thin-section CT scans of 17 discharged patients with COVID-19 were obtained during recovery. Longitudinal changes of clinical parameters and CT pattern were documented in all patients during 4 weeks since admission. CT score was used to evaluate the extent of the disease. There was a marked improvement of fever, lymphocytes count, C-reactive protein and erythrocyte sedimentation rate within the first two weeks since admission. However, the mean CT score rapidly increased from the 1^st to 3^rd week, with a top score of 8.2 obtained in the 2^nd week. During the 1^st week, the main CT pattern was ground-glass opacities (GGO,76.5%). The frequency of GGO (52.9%) decreased in the 2^nd week. Consolidation and mixed patterns (47.0%) were noted in the 2^nd week. Thereafter, consolidations generally dissipated into GGO and the frequency of GGO increased in the 3^rd week (76.5%) and 4^th week (71.4%). Opacities were mainly located in the peripheral (76.5%), subpleural (47.1%) zones of the lungs, and presented as focal (35.3%) or multifocal (29.4%) in the 1^st week and became more diffuse in the 2^nd (47.1%) and 3^rd week (58.8%), then showed reduced extent in 4^th week (50%). The progression course of CT pattern was later than the clinical parameters within the first two weeks since admission; however, there was a synchronized improvement in both clinical and radiologic features in the 4^th week.